This course focuses on overcoming common data problems, specifically the removal of duplicates, using the R programming language. Data scientists often spend a significant amount of time cleaning and manipulating data, as it is crucial for accurate analysis. By learning various techniques in this course, you will be able to effectively clean dirty data using R. The course begins by teaching you how to convert data types, apply range constraints, and handle both full and partial duplicates. These techniques will help you avoid double-counting and ensure the accuracy of your analysis. Once you have mastered the basics, the course progresses to more advanced challenges, such as maintaining consistency in measurements and dealing with missing data. Each new concept is reinforced through hands-on exercises, allowing you to solidify your understanding and gain practical experience. In the final chapter, you will learn about record linkage, a technique used to merge datasets with issues like typos or different spellings. You will explore the application of record linkage by joining two restaurant review datasets into a single dataset. By completing this course, you will acquire the skills necessary to overcome common data problems and enhance the quality of your analysis using R.